IEEE Symposium on Industrial Electronics and Applications 

About: IEEE Symposium on Industrial Electronics and Applications is an academic conference. The conference publishes majorly in the area(s): Control theory & AC power. Over the lifetime, 698 publications have been published by the conference receiving 4828 citations.

RFID based attendance system

Abstract: Most educational institutions' administrators are concerned about student irregular attendance. Truancies can affect student overall academic performance. The conventional method of taking attendance by calling names or signing on paper is very time consuming and insecure, hence inefficient. Radio Frequency Identification (RFID) based attendance system is one of the solutions to address this problem. This system can be used to take attendance for student in school, college, and university. It also can be used to take attendance for workers in working places. Its ability to uniquely identify each person based on their RFID tag type of ID card make the process of taking the attendance easier, faster and secure as compared to conventional method. Students or workers only need to place their ID card on the reader and their attendance will be taken immediately. With real time clock capability of the system, attendance taken will be more accurate since the time for the attendance taken will be recorded. The system can be connected to the computer through RS232 or Universal Serial Bus (USB) port and store the attendance taken inside database. An alternative way of viewing the recorded attendance is by using HyperTerminal software. A prototype of the system has been successfully fabricated.

A novel Maximum Power Point tracking control of photovoltaic system under partial and rapidly fluctuating shadow conditions using Differential Evolution

Abstract: Photovoltaic (PV) system performance extremely depends on local insolation and temperature conditions. Under partial shading, P-I characteristics of PV systems are complicated and may have multiple local maxima. Conventional Maximum Power Point Tracking (MPPT) techniques can easily fail to track global maxima and may be trapped in local maxima under partial shading; this can be one of main causes for reduced energy yield for many PV systems. In order to solve this problem, this paper proposes a novel Maximum Power Point tracking algorithm based on Differential Evolution (DE) that is capable of tracking global MPP under partial shaded conditions. The ability of proposed algorithm and its excellent performances are evaluated with conventional and popular algorithm by means of simulation. The proposed algorithm works in conjunction with a Boost (step up) DC-DC converter to track the global peak. Moreover, this paper includes a MATLAB-based modeling and simulation scheme suitable for photovoltaic characteristics under partial shading.

A comparative study of 6T, 8T and 9T decanano SRAM cell

Abstract: Data retention and leakage current reduction are among the major area of concern in today's CMOS technology. In this paper 6T, 8T and 9T SRAM cell have been compared on the basis of read noise margin (RNM), write noise margin (WNM), read delay, write delay, data retention voltage (DRV), layout and parasitic capacitance. Corner and statistical simulation of the noise margin has been carried out to analyze the effect of intrinsic parameter fluctuations. Both 8T SRAM cell and 9T SRAM cell provides higher read noise margin (around 4 times increase in RNM) as compared to 6T SRAM cell. Although the size of 9T SRAM cell is around 1.35 times higher than that of the 8T SRAM cell but it provides higher write stability. Due to single ended bit line sensing the write stability of 8T SRAM cell is greatly affected. The 8T SRAM cell provides a write “1” noise margin which is approximately 3 times smaller than that of the 9T SRAM cell. The data retention voltage for 8T SRAM cell was found to be 93.64mV while for 9T SRAM cell it was 84.5mV and for 6T SRAM cell it was 252.3mV. Read delay for 9T SRAM cell is 98.85ps while for 6T SRAM cell it is 72.82ps and for 8T SRAM cell it is 77.72ps. The higher read delay for 9T SRAM cell is attributed to the fact that dual threshold voltage technology has been in it in order to reduce the leakage current. Write delay for 9T SRAM cell was found to be 10ps, 45.47ps for 8T SRAM cell and 8.97ps for 6T SRAM cell. The simulation has been carried out on 90nm CMOS technology. .

Design of AES S-box using combinational logic optimization

Abstract: Advanced Encryption Standard (AES) is one of the most common symmetric encryption algorithms. The hardware complexity in AES is dominated by AES substitution box (S-box) which is considered as one of the most complicated and costly part of the system because it is the only non-linear structure. The proposed work employs a combinational logic design of S-Box implemented in Virtex II FPGA chip. The architecture employs a Boolean simplification of the truth table of the logic function with the aim of reducing the delay. The S-Box is designed using basic gates such as AND gate, NOT gate, OR gate and multiplexer. Theoretically, the design reduces the overall delay and efficiently for applications with high-speed performance. This approach is suitable for FPGA implementation in term of gate area. The hardware, total area and delay are presented.

Comparison of different wavelet features from EEG signals for classifying human emotions

Abstract: In recent years, estimation of human emotions from Electroencephalogram (EEG) signals plays a vital role on developing intellectual Brain Computer Interface (BCI) devices. In this work, we have collected the EEG signals using 64 channels from 20 subjects in the age group of 21~39 years for determining discrete emotions (happy, surprise, fear, disgust, and neutral) under audio-visual induction (video/film clips) stimuli. Surface Laplacian filtering is used to preprocess the EEG signals and decomposed into five different EEG frequency bands (delta, theta, alpha, beta, and gamma) using Wavelet Transform (WT). The statistical features are derived from all these five frequency bands are considered for classifying the emotions using two linear classifiers (K Nearest Neighbor (KNN) & Linear Discriminant Analysis (LDA)). The main objective of this work is to consider a selected number of 24 channels for assessing emotions from the original EEG channels. There are three different wavelet functions (“db8”, “sym8”, and “coif5”) are used to derive the linear and non linear features for emotion classification. The validation of statistical features is performed using 5 fold cross validation. In this work, KNN outperforms LDA by offering a maximum average classification rate of 79.174 %. Finally we present the average and individual classification rate of emotions over various statistical features on three different wavelet functions for justifying the performance of our emotion recognition system.